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1. |
Record Nr. |
UNINA9910698197203321 |
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Titolo |
Compilation of water-resources data for Montana, water year 2007 [[electronic resource] /] / compiled by P.B. Ladd ; prepared in cooperation with the State of Montana and other agencies |
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Pubbl/distr/stampa |
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Reston, Va. : , : U.S. Dept. of the Interior, U.S. Geological Survey, , [2008] |
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Descrizione fisica |
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1 electronic text (1 volumes (various pagings)) : HTML, digital, PDF file |
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Collana |
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Open-file report ; ; 2008-1325 |
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Altri autori (Persone) |
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Soggetti |
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Hydrology - Montana |
Streamflow - Montana |
Water quality - Montana |
Water-supply - Montana |
Statistics. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Title from PDF title screen (viewed on Mar. 5, 2009). |
Chiefly tables. |
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Nota di bibliografia |
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Includes bibliographical references. |
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2. |
Record Nr. |
UNISA996647866603316 |
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Autore |
Singh Hemant |
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Titolo |
Evolutionary Multi-Criterion Optimization : 13th International Conference, EMO 2025, Canberra, ACT, Australia, March 4–7, 2025, Proceedings, Part I / / edited by Hemant Singh, Tapabrata Ray, Joshua Knowles, Xiaodong Li, Juergen Branke, Bing Wang, Akira Oyama |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025 |
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ISBN |
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Edizione |
[1st ed. 2025.] |
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Descrizione fisica |
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1 online resource (498 pages) |
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Collana |
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Lecture Notes in Computer Science, , 1611-3349 ; ; 15512 |
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Altri autori (Persone) |
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RayTapabrata |
KnowlesJoshua |
LiXiaodong |
BrankeJuergen |
WangBing |
OyamaAkira |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Artificial Intelligence |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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-- Algorithm design. -- Towards an Efficient Innovation Path Seeking Algorithm Using Directed Domination. -- An MaOEA/Local Search Hybrid Based on a Fast, Stochastic BFGS Using Achievement Scalarizing Search Directions. -- Selective evaluations for expediting multi-objective bilevel optimization. -- MOAISDX: A New Multi-objective Artificial Immune System based on Decomposition. -- PAES-25: Local Search, Archiving and Multi/Many-objective PseudoBoolean Functions. -- Weights-Guided Random Bit Climber for Binary Many-objective Optimization. -- Bilevel Optimization-based Decomposition for Solving Single and Multi objective Optimization Problems. -- A Study on Optimistic & Pessimistic Pareto-fronts in Multi objective Bilevel Optimization via ?-Perturbation. -- Cumulative Step Size Adaptation for Adaptive SEMO in Integer Space. -- Adaptive Normal-Boundary Intersection Directions for Evolutionary Many objective Optimization |
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with Complex Pareto Fronts. -- Encodings for Multi-Objective Free-Form Coverage Path Planning. -- VBEA: Voting-Based Evolutionary Algorithm for Multi-Objective Planning. -- Enhancing NSGA-II with a Knee Point for Constrained Multi-objective Optimization. -- Benchmarking. -- Single and Multi-Objective Optimization Benchmark Problems Focusing on Human-Powered Aircraft Design. -- An Extension of the Welded Beam Problem that Includes Multiple Interactng Design Concepts. -- Extended Results on Analytical Hypervolume Indicator Calculation of Linear and Quadratic Pareto Fronts. -- MO-IOHinspector: Anytime Benchmarking of Multi-Objective Algorithms using IOH profiler. -- Applications. -- Multi-Objective Sequential Decision Making for Holistic Supply Chain Optimization. -- Interactive evolutionary re optimization for ground fish survey planning. -- A Multi-Objective Competitive Co-Evolutionary Framework with Progressive Shrinking for Wargame Scenarios. -- -- Impact of Environmental Changes on Optimized Robotics Collective Motion for Multi-Objective Coverage Tasks. |
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Sommario/riassunto |
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This two-volume set LNCS 15512-15513 constitutes the proceedings of the 13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025, held in Canberra, ACT, Australia, in March 2025. The 38 full papers and 2 extended abstracts presented in this book were carefully reviewed and selected from 63 submissions. The papers are divided into the following topical sections: Part I : Algorithm design; Benchmarking; Applications. Part II : Algorithm analysis; Surrogates and machine learning; Multi-criteria decision support. |
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